Diffusion tensor magnetic resonance imaging (DTI) facilitates provides the ability to quantitatively assess white matter integrity. One area where DTI has been used has resulted in the expansion of the boundaries of diagnostic imaging by examining the diffusion of water in brain tissue. Recent studies have shown DTI can be used to provide diagnosis of disease conditions in cerebral ischemia, acute stroke, and multiple sclerosis. Its diagnostic value is based primarily on restricted water movement due to myelin found in white matter. The restricted water movement causes anisotropic diffusion. The degree of directionality of the flow is termed anisotropy, which is the coefficient of variation of the eigenvalues of the diffusion tensor. Fractional anisotropy (FA) has been implicated to being sensitive to changes in white matter integrity. FA loss and apparent diffusion coefficient (Dav) rise have been demonstrated in a number of traumatic brain injury studies. It is natural to assume that these quantitative characteristics are indicative of white matter damage in a variety of pathologies.
The quantification of DTI for investigating white matter abnormalities is generally approached using one of two methods. These methods are voxel-based analysis (VBA) where data sets are compared at a voxel by voxel scale and region of interest (ROI) analysis. In cases where it is impractical to predict anatomical domains of damage, researchers tend to use a voxel-based approach to characterize statistical differences between groups. VBA involves spatial normalization of brain images to a stereotactic 3D space. In order to produce a more normalized distribution of image data, a smoothing function is applied to the images. Statistical differences between groups are then made on a voxel by voxel basis to determine variations in tissue composition.
While a voxel based strategy has the advantage of evaluating the brain in a model-free manner, and is therefore suitable for the identification of unexpected areas of white matter pathology, there are several limitations. A central disadvantage of this approach is that differences in gross anatomical morphology among subjects may influence spatial normalization and thus artificially inflate measurement differences. Moreover, many normalization algorithms use smoothing functions which introduce “blur” into the image, and violates the original uniformity of voxel size present in the original images, thereby creating noise in the measurement. Researchers have suggested the possibility that VBA has reduced and inconsistent sensitivity in specific regions of the brain, especially those with greater anatomical variability. Furthermore, VBA is only applicable for group analyses so its clinical application for individual subject analysis has not yet been convincingly demonstrated. Most importantly, VBA is not a genuine quantitative analysis of tissue differences, but a more qualitative analysis of regional variations.
Most researchers use ROI analysis when it is possible to hypothesize specific areas of the brain that are implicated in disease. However, ROI analysis normally requires manual tracing readily identifiable regions. Current methods for obtaining these boundaries are principally manual and subjective. These methods include hand drawn ROI analysis where investigators draw polygons over one of many potential 2 dimensional MR images. Manual tracing of white matter structures in the brain also is very time consuming, requiring expert contribution to accurately identify structure boundaries. The manual tracing can take up to several hours per case. For ROI analysis, researchers identify brain regions and compare FA and Dav between research subjects and normal controls. Selection bias and variability in the process of selecting images and drawing ROI's introduces significant barriers to both research methodology and clinical assessment. Conventional clinical analysis does not include a standardized quantitative protocol that can be applied to a variety of conditions.
From the above, it can be seen that there is a need for a rapid automated or semi-automated method for white matter quantification for both individual and group analysis.